1. Field of the Invention
The present invention relates to a method for predicting petrophysical characteristics of an underground reservoir, comprising constructing and updating a geologic model constrained by data in time such as seismic data.
This methodology is part of the set of themes relative to reservoir characterization, whose objective is to provide reliable information on reservoirs in order to better predict their behaviour and to optimize their development scheme.
2. Description of the Prior Art
Updating geologic models is based on the inverse problem theory: some parameters of the geologic model, such as porosity or permeability, are adjusted iteratively to fit observation data, such as seismic data for example. As in any inverse problem, there is not one and only solution. In order to reduce uncertainties on the production prediction, it is necessary to integrate more observation data (logs, production data, seismic data, . . . ), which allows to better constrain the models.
Geologic models are a fine representation of the structure and of the behavior of an underground reservoir. These models consist of a set of cells, also referred to as grid cells, that form a grid. This grid represents a discretization of the reservoir and it has to be representative of the reservoir structure. In order to be able to reproduce the static or dynamic behaviour of a reservoir, two or three-dimensional property “maps” describing the static and/or dynamic behavior of the reservoir are associated with these models, referred to as geocellular models. It is thus well known to use stochastic simulation methods (Journel A., Huijbregts C., 1978, “Mining Geostatistics”, Centre de Géostatistique et de Morphologie Mathematique, London, Academic press). During these simulations, a property field or map constrained by reservoir data measured at the level of wells for example is produced.
Simultaneous integration, in the stochastic simulations, of several types of data, whether static or dynamic, allows considerable reduction of the space of the allowable geologic models and therefore to better predict the behaviour of the reservoirs studied. Some methods already use combined integration of geologic and seismic data. The seismic data recorded are the seismic amplitudes. A large number of attributes, referred to as seismic attributes, can be calculated from the latter to improve the raw data (amplitudes) interpretation. The most commonly used attributes are the impedances. They are generally determined from a seismic amplitude stratigraphic inversion technique. Such an approach is proposed for example by Tonellot et al. in the following document for example:    Tonellot, T., Macè, D., and Richard, V., 2001, Joint Stratigraphic Inversion of Angle Limited Stacks, 71st Ann. Internal. Mtg., Soc. Expl. Geophys., Expanded Abstracts, AVO 2.6, 227-230.
Impedances can also be obtained on a fine scale by means of stochastic techniques. Such a technique is proposed by Bortoli et al. in the following document for example:    Bortoli, L. J., Alabert F., Haas A. et Journel, A. G. 1993. Constraining Stochastic Images to Seismic Data. Geostatistics Troia 82, A. Soares (ed.), 325-337. Kluwer, Dordrecht.
The seismic amplitudes are here stochastically inverted on a fine grid. This approach allows to obtain several impedance cubes, all in accordance with the seismic amplitudes.
The seismic constraint (data resulting from the seismic amplitudes that the geologic model must meet) is often introduced as a secondary variable guiding the distribution of the variable of interest. Techniques known to the man skilled in the art, such as simulation techniques with external drift, or co-simulations using one or more seismic attributes as secondary variables, allow to distribute reservoir properties constrained by these seismic attributes. The following document can for example be mentioned:    Doyen P. M., 1988. Porosity from Seismic Data: A Geostatistical Approach. Geophysics 53(10), 1263-1275.
Another technique for using these seismic impedances to constrain geologic models is proposed in the following documents for example:    Matheron G., Beucher H., de Fouquet C., Galli A., Guerillot D., and Ravenne C. 1987: Conditional Simulation of the Geometry of Fluvio Deltaic Reservoirs. SPE 62nd Annual Conference, Dallas, Tex., pp. 591-599,    Matheron G., Beucher H., de Fouquet C., Galli A and Ravenne C., 1988: Simulation Conditionnelle à Trois Faciès Dans Une Falaise de la Formation du Brent. Sciences de la Terre, Série Informatique Géologique, 28, pp. 213-249,    Galli A., Beucher H., Le Loc'h G., Doligez B. and Heresim group, 1993: the Pros and Cons of the Truncated Gaussian Method. In: Armstrong M. and Dowd P. A. (eds), Geostatistical Simulations, pp. 217-233, Kluwer Academic Publishers, 1993.
These documents use the technique referred to as “thresholded Gaussian”. In this approach, seismic attributes are used to establish a lithofacies proportion curve matrix. This matrix then allows the spatial distribution of the lithofacies to be guided.
However, none of these aforementioned techniques allows to provide consistency between the geologic model thus obtained and the seismic impedances.
Thus, current techniques propose constructing geocellular reservoir models by means of stochastic simulations allowing spatial distribution of the lithofacies and/or of the reservoir properties (porosity and permeability for example). These simulations are constrained by well data, by geostatistical model parameters and possibly guided by one or more seismic attributes. However, it is not guaranteed that the geologic model obtained reproduces the seismic impedances obtained from the observed seismic data. In general terms, no method guarantees that the geologic model obtained reproduces the seismic data, that is a modelling of the seismic amplitudes from the geologic model does not provide a result that is consistent with the amplitudes measured during the seismic survey.
The proposed methodology allows obtaining geologic models consistent with the seismic data measured during the seismic survey.